Systems and methods for processing electronic images to model nutrient transport

ABSTRACT

Systems and methods are disclosed for identifying and modeling unresolved vessels, and the effects thereof, in image-based patient-specific hemodynamic models. One method includes: receiving a patient-specific anatomical model of at least a portion of a visceral vascular system of the patient; receiving patient-specific information related to the patient&#39;s food intake; generating a patient-specific model of blood flow in the patient-specific anatomical model of the portion of the visceral vascular system of the patient; generating a patient-specific model of nutrient transport from at least a part of a gastrointestinal system of the patient to the portion of the visceral vascular system of the patient based on the patient-specific information related to the patient&#39;s food intake; and determining an indicia of energy available in the patient based on the patient-specific model of nutrient transport.

RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.62/373,012 filed Aug. 10, 2016, the entire disclosure of which is herebyincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

Various embodiments of the present disclosure relate generally tomodeling of physiological systems. More specifically, particularembodiments of the present disclosure relate to systems and methods formodeling nutrient transport and/or predicting weight change.

BACKGROUND

The visceral system plays an important role in the transfer of nutrientsfrom food to the blood stream, and therefore in weight loss and/orweight gain in individuals. Various factors may contribute to themechanism by which weight loss and/or weight gain occurs, at leastwithin the visceral vascular system. These factors may include: (i) theefficiency of digestive enzymes, (ii) the diffusion of the nutrientsinto the blood stream; (iii) the flow rate through the small intestine,mesenteric artery, celiac artery, iliac artery, the portal veins, and/orthe hepatic veins; (iv) the characteristics of the arteries and/orveins; (v) the characteristics of blood transport and cellularmetabolism, including efficiency; (vi) evidence and/or characteristicsof peristalsis; (vii) the basal metabolic rate; and/or (viii) anypreexisting conditions, e.g., insulin resistance. More specifically,energy and metabolic efficiency may be understood by the mechanism ofenergy transfer in the visceral vascular system, and the processing ofnutrients in the liver.

One of the causes for weight loss may be related to mesenteric ischemia.Mesenteric ischemia may present itself as postprandial pain due to theinability for blood flow in mesenteric artery to increase in response tothe need for increased blood flow during the digestive process, thusimpairing the ability to absorb nutrients from the small intestine. Inother words, the arterial blood flow may not be able to meet visceralblood demand. Weight loss may also be caused by obstruction in theportal venous system or hepatic artery, impairing the delivery ofnutrients to the liver which processes the absorbed nutrients. Theinability to process nutrients in the liver, e.g., during insulinresistance, may also contribute to weight loss or gain.

SUMMARY

Described below are various embodiments of the present disclosure of asystem and method that aids in modeling blood flow and its relationshipto weight gain or loss. According to certain aspects of the presentdisclosure, systems and methods are disclosed for modeling nutrienttransport within a patient.

One method includes: receiving, in an electronic storage medium, apatient-specific anatomical model of at least a portion of a visceralvascular system of the patient; receiving, in an electronic storagemedium, patient-specific information related to the patient's foodintake; generating a patient-specific model of blood flow in thepatient-specific anatomical model of the portion of the visceralvascular system of the patient; generating a patient-specific model ofnutrient transport from at least a part of a gastrointestinal system ofthe patient to the portion of the visceral vascular system of thepatient based on the patient-specific information related to thepatient's food intake; and determining an indicia of energy available inthe patient based on the patient-specific model of nutrient transport.

In accordance with another embodiment, a system for modeling nutrienttransport within a patient comprises: a data storage device storinginstructions for modeling nutrient transport within a patient; and aprocessor configured for: receiving, in an electronic storage medium, apatient-specific anatomical model of at least a portion of a visceralvascular system of the patient; receiving, in an electronic storagemedium, patient-specific information related to the patient's foodintake; generating a patient-specific model of blood flow in thepatient-specific anatomical model of the portion of the visceralvascular system of the patient; generating a patient-specific model ofnutrient transport from at least a part of a gastrointestinal system ofthe patient to the portion of the visceral vascular system of thepatient based on the patient-specific information related to thepatient's food intake; and determining an indicia of energy available inthe patient based on the patient-specific model of nutrient transport.

In accordance with another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for performing a method ofmodeling nutrient transport within a patient, the method comprising:receiving, in an electronic storage medium, a patient-specificanatomical model of at least a portion of a visceral vascular system ofthe patient; receiving, in an electronic storage medium,patient-specific information related to the patient's food intake;generating a patient-specific model of blood flow in thepatient-specific anatomical model of the portion of the visceralvascular system of the patient; generating a patient-specific model ofnutrient transport from at least a part of a gastrointestinal system ofthe patient to the portion of the visceral vascular system of thepatient based on the patient-specific information related to thepatient's food intake; and determining an indicia of energy available inthe patient based on the patient-specific model of nutrient transport.

According to certain aspects of the present disclosure, systems andmethods are disclosed for planning treatment of a lesion.

One method includes: receiving, in an electronic storage medium, apatient-specific anatomical model of at least a portion of a visceralvascular system of a patient; identifying one or more lesions in thepatient-specific anatomical model suspected of affecting blood flow toat least a part of the gastrointestinal system of the patient; andperforming one or more iterations of: (1) selecting one or more lesionsof the identified lesions; (2) determining a healthy diameter of a bloodvessel lumen at a location of the one or more lesions of the identifiedlesions; (3) generating an anatomical model of a treatment of the one ormore lesions using the determined healthy diameter; and (4) determiningand outputting, into an electronic storage medium, an indicia of theenergy available in the patient, based on the treatment of the one ormore lesions.

In accordance with another embodiment, a system for planning treatmentof a lesion comprises: a data storage device storing instructions forplanning treatment of a lesion; and a processor configured for:receiving, in an electronic storage medium, a patient-specificanatomical model of at least a portion of a visceral vascular system ofa patient; identifying one or more lesions in the patient-specificanatomical model suspected of affecting blood flow to at least a part ofthe gastrointestinal system of the patient; and performing one or moreiterations of: (1) selecting one or more lesions of the identifiedlesions; (2) determining a healthy diameter of a blood vessel lumen at alocation of the one or more lesions of the identified lesions; (3)generating an anatomical model of a treatment of the one or more lesionsusing the determined healthy diameter; and (4) determining andoutputting, into an electronic storage medium, an indicia of the energyavailable in the patient, based on the treatment of the one or morelesions.

In accordance with another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for planning treatment of alesion, the method comprising: receiving, in an electronic storagemedium, a patient-specific anatomical model of at least a portion of avisceral vascular system of a patient; identifying one or more lesionsin the patient-specific anatomical model suspected of affecting bloodflow to at least a part of the gastrointestinal system of the patient;and performing one or more iterations of: (1) selecting one or morelesions of the identified lesions; (2) determining a healthy diameter ofa blood vessel lumen at a location of the one or more lesions of theidentified lesions; (3) generating an anatomical model of a treatment ofthe one or more lesions using the determined healthy diameter; and (4)determining and outputting, into an electronic storage medium, anindicia of the energy available in the patient, based on the treatmentof the one or more lesions.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments,and together with the description, serve to explain the principles ofthe disclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network forpredicting weight gain and/or weight loss through modeling the visceralsystem, according to an exemplary embodiment of the present disclosure.

FIG. 2 is an illustration of at least some of the vessels and otheranatomical parts of the visceral system that are pertinent to predictingweight gain or weight loss, according to an exemplary embodiment of thepresent disclosure.

FIG. 3 is a block diagram of a general method of predicting weight gainand/or weight loss through modeling the visceral system, according to anexemplary embodiment of the present disclosure.

FIG. 4 is a block diagram of a general method 400 of modeling nutrientintake from parts of the gastrointestinal (GI) tract (e.g., the smalland large intestines) to the blood vessels (e.g., the portal vein).Method 400 may describe the process of performing step 310 in method 300in further detail.

FIG. 5 is a block diagram of a general method 500 of modeling blood flowthrough the hepatic and portal vessels, and the phasic changes in bloodflow in relation to food intake. Method 500 may describe the process ofperforming step 312 in method 300 in further detail.

FIG. 6 is a block diagram of an exemplary method of training andapplying a machine learning algorithm using boundary conditions to solvefor blood flow and blood pressure, according to an exemplary embodimentof the present disclosure. FIG. 6 may depict an exemplary method ofperforming step 308 of method 300 in FIG. 3.

FIG. 7 is a block diagram of an exemplary method of determining lesionsthat result in weight loss (e.g., via mesenteric, hepatic, and/or portalischemia), according to an exemplary embodiment of the presentdisclosure.

The steps described in the methods may be performed in any order, or inconjunction with any other step. It is also contemplated that one ormore of the steps may be omitted for performing the methods described inthe present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

Various embodiments of the present disclosure may provide systems andmethods for modeling nutrient transport and/or predicting weight change,e.g., through modeling the visceral system. Some embodiments of thepresent disclosure may include a patient-specific prediction of energytransfer and metabolism by modeling the visceral and the portal system.At least some of the systems and methods of the present disclosure mayinclude a predictive modeling approach that accounts for flow-ratesthrough various vessels (e.g., in the visceral vascular system and thehepatic arteries and veins), diffusion and/or transport through thevilli and micro-villi, and peristalsis to quantify the fraction ofintake energy that is converted into useful energy. Some embodiments mayinclude a system and method of predicting the propensity for weight gainor loss by modeling the mismatch between the net energy transferred andenergy demand and metabolism. Some embodiments may include a system andmethod that may take as an input data from any imaging modality,including, but not limited to, CT scan, ultrasound, angiogram, MRI etc.,from which the geometry of all the vessels of interest in the visceralsystem may be extracted. Subsequently, the blood demand in each of thevessels (e.g., mesenteric, celiac, iliac etc.) may be modeled. Blooddemand may be based on scaling laws based on patient and organ size,direct measurement of flow using techniques such as Doppler ultrasound,or by scaling cardiac output with population-averaged flow-splits. Theblood demand in the visceral system may also be driven by hormonalsignals related to food ingestion and stage of digestive cycle, such aspostprandial hyperemia. Artery size may change during fasting, and/orwhen CT scan is performed, but may increase in size after meals, as theblood flow increases. The effect of postprandial hyperemia may also betaken into account when modeling organ demand. The spatial distributionof flow-rates may be obtained by using computational fluid dynamicsmethods (e.g., using boundary conditions directly derived as describedabove). In some embodiments, the spatial distribution of flow-rates maybe obtained by using reduced order models of blood flow, machinelearning methods, etc. The absorption of nutrients into the blood streammay occur through villi and microvilli, distributed through the smallintestine. The breakdown of food into nutrients may occur in the smallintestine. The nutrients may further diffuse or be transported into themain blood stream. A fast rate of flow (e.g. liquids) in the smallintestine may hinder the proper absorption of nutrients into the bloodstream. Blockage in any of the arteries may result in insufficientnutrient absorption, which may result in a poor metabolic state.Further, the nutrients may be transported to the liver using the portalveins, which may then be delivered to the systemic circulation via thehepatic veins. By modeling blood flow patterns downstream, we may alsobe able to assess and identify specific regions based on the nutrientsupply and demand.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system 100 and network for predicting weight gain and/orweight loss through modeling the visceral system, according to anexemplary embodiment. Specifically, FIG. 1 depicts a plurality ofphysicians 102 and third party providers 104, any of whom may beconnected to an electronic network 100, such as the Internet, throughone or more computers, servers, and/or handheld mobile devices.Physicians 102 and/or third party providers 104 may create or otherwiseobtain images of one or more patients' anatomy. The physicians 102and/or third party providers 104 may also obtain any combination ofpatient-specific and/or reference anatomical images and/or information,including, but not limited to, geometrical and/or anatomicalcharacteristics of the vessels of interest of a patient (e.g.,mesenteric artery, celiac artery, iliac artery, portal vein, hepaticartery, hepatic vein, etc.).

Physicians 102 and/or third party providers 104 may transmit theanatomical images and/or information on vessels of interest to serversystems 106 over the electronic network 100. Server systems 106 mayinclude storage devices for storing images and data received fromphysicians 102 and/or third party providers 104. Server systems 106 mayalso include processing devices for processing images and data stored inthe storage devices.

FIG. 2 is an illustration of at least some of the vessels and otheranatomical parts of the cardiovascular and visceral system that arepertinent to predicting weight gain or weight loss, according to anexemplary embodiment of the present disclosure. The visceral system mayinclude , various parts of the GI tract (e.g., intestines 208 (small andlarge), esophagus, stomach, pancreas, etc.), and the liver 214. Thevisceral vascular system depicted in FIG. 2 may include the heart, andvarious blood vessels servicing and/or transporting nutrients from or tothe organs of the visceral system. For example, the abdominal aorta 204may carry blood towards the hepatic artery 212 and mesenteric artery206. The hepatic artery 212 may carry blood to the liver 214 and themesenteric artery 206 may carry blood to parts of the GI tract (e.g.,intestines 208). The mesenteric artery 206 and/or emanating vessels fromthe mesenteric artery 206 (e.g., capillaries, venules, portal veins 210,etc.) may receive nutrients from parts of the GI tract (e.g., intestines208). Once the nutrients are received, the resulting vessels (e.g.,portal veins 210) may deliver blood and nutrients to the liver 214,where some nutrients may be processed and where blood (e.g., itscontents) may be metabolized. Thereafter, the resulting nutrients may becarried by the hepatic veins 216 towards the inferior vena cava 218 andto the heart 202 and then on to the systemic circulation. In variousembodiments, the systemic circulation may refer to the overallcirculatory system that carries oxygenated blood throughout the body andreturns deoxygenated blood the heart. Moreover, the net energy availablein the systemic circulation after nutrient transport may be used topredict a change in a weight or mass of a patient following a foodintake.

Thus, in some embodiments, the liver may receive oxygenated blood fromthe hepatic artery and nutrient filled, non-oxygenated blood via theportal vein, as the blood in the portal veins may have been deoxygenatedgoing through the capillary bed in the intestines.

Other parts of the visceral system not shown in FIG. 2 may be useful andconsidered in embodiments of the present disclosure. These partsinclude, for example, the pancreas and the stomach. For example, bloodmay flow towards the pancreas via the pancreaticoduodenal vessels, whichmay arise from the celiac artery. Inadequate nutrient blood flow to thepancreas could impair the production of digestive enzymes, normallyproduced by the pancreas. These digestive enzymes may assist in thedigestive process in various parts of the GI tract (e.g., in theduodenum and small intestine). For example, while the stomach may aid indigestion by breaking down ingested food in the stomach's acidicenvironment, the ingested nutrients may be further digested and/orprocessed for absorption using the digestive enzymes at the microvilli,which is downstream of the stomach in the GI tract. As the food isdigested and/or processed at the microvilli, nutrients may enter theportal venous system.

Still referring to FIG. 2, at least some embodiments of the presentdisclosure may be explained as various methods further below, and thesemethods may reference various features of FIG. 2. For example, method300 of FIG. 3 provides a general embodiment for predicting weight gainand/or weight loss through modeling the visceral system, and includessolving for blood flow and pressure through a vessel of interest of apatient (e.g., in step 308), including, for example, the hepatic artery212, mesenteric artery 206, portal veins 210, and hepatic veins 216.Method 400 of FIG. 4 further describes the process of modeling nutrientintake into vessels emanating from the mesenteric artery 206 (e.g.,portal veins 210) at parts of the GI tract (e.g., intestines 208).Method 500 describes the process of modeling blood flow through thehepatic vessels and portal vessels (e.g., hepatic arteries 212, hepaticveins 216, portal veins 210, etc.) and any phasic changes in blood flowin relation to food intake. In some embodiments, method 500 mayincorporate information regarding the functioning of the liver 214.

FIG. 3 is a block diagram of a general method of predicting weight gainand/or weight loss through modeling the visceral system, according to anexemplary embodiment of the present disclosure. The method of FIG. 3 maybe performed by server systems 106, based on information, images, anddata received from physicians 102 and/or third party providers 104 overelectronic network 100.

Step 302 may include receiving an anatomical model that includes atleast the vessels of interest of a patient. In one embodiment, theanatomical model may be generated from received or stored anatomicimages and/or information encompassing the vessels of interest of apatient. In such embodiments, the anatomic images and/or information maybe stored in an electronic storage medium. The vessels of interest mayinclude, for example, the mesenteric artery, celiac artery, iliacartery, portal vein, hepatic artery, hepatic vein, and/or any vesselinvolved in the digestive process. The anatomical images and/orinformation may be extracted from images and/or image data generatedfrom a scanning modality (e.g., forms of magnetic resonance (MR), formsof computed tomography (CT), forms of positron emission tomography(PET), X-Ray, etc.) and/or may be received from an electronic storagedevice (e.g. hard drive). Step 302 may further include using theanatomical images and/or information to generate a model that includes,at least, the vessels of interest. The process of generating a model mayinclude various segmentation techniques to capture areas of interestfrom an image or image data.

Step 304 may include truncating the model at locations where appropriateboundary conditions may be applied. In some embodiments, the truncationmay be performed such that the regions encompassing the vessels ofinterest can be captured. For example, intensity variation may be usedto detect vessels of interest (e.g., the mesenteric artery, celiacartery, iliac artery, portal vein, hepatic artery, hepatic vein, etc.).In some embodiments, the truncation may be performed such that regionsof vessel narrowing may be captured. For example, the truncation may beperformed such that the regions distal to the locations of disease inthe arteries, and/or regions encompassing the blood vessels of interestcan be captured.

Step 306 may include applying appropriate boundary conditions at theboundaries. The boundary conditions provide information about thehemodynamics at the boundaries of the three dimensional model, e.g., theinflow boundaries or inlets, the outflow boundaries or outlets, thevessel wall boundaries, etc. The inflow boundaries or inlets may includethe boundaries through which flow is directed into the anatomy of thethree-dimensional model, such as at the aorta. The inflow boundary maybe assigned, e.g., with a prescribed value or field for velocity, flowrate, pressure, or other characteristic, for example, by coupling aheart model and/or a lumped parameter model to the boundary, etc. Theflow rate at the aorta may be estimated by cardiac output, measureddirectly or derived from the patient's mass using scaling laws. In someembodiments, flow rate of the aorta may be estimated by cardiac outputusing methods described in U.S. Pat. No. 9,424,395, incorporated byreference in entirety herein. This is described in sensitivity/guidedsensitivity patent.

For example, net cardiac output (Q) can be calculated from body surfacearea (BSA) as

$Q = {\frac{1}{60}{BSA}^{1.15}}$

(cardiac output). The body surface area (BSA) may be calculated fromheight (h) and weight (w) as:

${BSA} = {\sqrt{\frac{hw}{3600}}.}$

Coronary flow rate (q_(cor)) can be calculated from myocardial mass(m_(myo)) as:

${q_{cor} = {c_{dil}\frac{5.09}{60}m_{myo}^{0.75}}},$

where c_(dil) is a dilation factor. Thus, the flow in the aorta can beQ-q_(cor).

While the flow rate of the aorta may be estimated from the cardiacoutput, the distribution of flow in the various branches of the aortamay vary greatly depending on the physiologic state (rest vs exercise vspostprandial) of the patient. After eating, the demand of the visceralsystem for more blood flow via the celiac, superior mesenteric artery(SMA) and inferior mesenteric artery (IMA) vessels for food digestionmay increase. If this increase in blood flow in the post-prandial statecannot be achieved (ie, restricted arterial blood flow, then digestionand nutrient absorption may not take place, and this may result inweight loss. The “post-prandial hyperemic state” may be similar to thehyperemia which is needed by the heart and skeletal muscles duringexercise.

The outflow boundaries (e.g., outlets) may include the boundariesthrough which flow is directed outward from the anatomy of the model,such as towards vessels of interest. Each outflow boundary can beassigned, e.g., by coupling a lumped parameter or distributed (e.g., aone-dimensional wave propagation) model. The outlet conditions mayinclude the blood pressure, flow rate, or a combination thereof (e.g.resistance, which is the ratio of pressure to flow). The prescribedvalues for the inflow and/or outflow boundary conditions may bedetermined by noninvasively measuring physiologic characteristics of thepatient, such as, but not limited to, cardiac output (the volume ofblood flow from the heart), blood pressure, etc. The vessel wallboundaries may include the physical boundaries of the vessels ofinterest.

Furthermore, the blood flow rate, velocity, and blood pressure in themesenteric artery may be quantified, and may be used as a predictor ofmesenteric ischemia. The effect of postprandial hyperemia along withorgan size and demand may be used to model the boundary conditions. Forexample. baseline flow may be modeled using fasting mesenteric flowconditions. The boundary conditions would then be changed to reflect thepostprandial maximal hyperemic state. Larger organs would have largervessels supplying them at rest

It is contemplated that in various embodiments or circumstances, theremay be a relationship between artery size, flow demand and downstreamboundary condition. Postprandial hyperemia may result in an increase inthe size of the mesenteric arteries, relative to the other arterieswhich may not be at hyperemic state. Hence, the boundary resistance maybe modeled as being inversely proportional to the outlet area. Further,the baseline arterial size may be reflective of the flow demand.

Step 308 may include solving for blood flow and blood pressure throughat least parts of the system using one or more of computational fluiddynamics, reduced order models, machine learning methods, etc. Forexample, step 308 may include using the boundary conditions calculatedin step 306 to solve the equations governing blood flow for velocity andpressure. In one embodiment, step 308 may include the computing of ablood flow velocity field or flow rate field for one or more points orareas of the anatomic model, using the assigned boundary conditions.This velocity field or flow rate field may be the same field as computedby solving the equations of blood flow using the physiological and/orboundary conditions provided above. Step 308 may further include solvingscalar advection-diffusion equations governing blood flow at one or morelocations of the patient-specific anatomic model.

Alternatively or additionally, the vessels of interest may be simplifiedto a lumped parameter and/or reduced order model, e.g., an electriccircuit. The calculated boundary conditions from step 306, pressuredifferences, and/or stenotic segments may be modeled as variouscomponents on the lumped parameter and/or reduced order model, e.g.,resistors on an electronic circuit.

Alternatively or additionally, the boundary conditions calculated instep 306 may be incorporated into or may be used to form feature vectorsto predict blood flow and/or blood pressure at one or more points of amodel. In such embodiments, population-based measurements of boundaryconditions, blood flow, and blood pressure may be used, for example, intraining a machine learning algorithm. Method 600 as depicted in FIG. 6further describes solving for blood flow and blood pressure usingmachine learning methods.

Step 310 may include modeling nutrient transport and/or net energytransfer from, for example, the small intestines to blood vessels. Theblood vessels may include, for example, the mesenteric artery, iliacartery, celiac artery, and/or vessels emanating from the arteries (e.g.,portal veins, capillaries, etc.). It is also contemplated that in someembodiments, nutrient transport and/or net energy transfer from otherparts of the GI tract (e.g., stomach, large intestines, etc.) to bloodvessels may also be modeled. The concentration of nutrients as they passthrough the intestine may be modeled using the following differentialequation that combines convection and dispersion of the nutrients withinthe intestine with the dispersion of nutrients across the villi andmicrovilli. This differential equation may be written as ([1,2,3])

$\begin{matrix}{{A\frac{dc}{dt}} = {{AD\frac{d^{2}c}{dt^{2}}} - {Q\frac{dc}{dx}} - {Pc\frac{dA}{dr}}}} & (1)\end{matrix}$

where c is the concentration of nutrients in the intestine, A is thecross sectional area, D is the molecular diffusivity, Q is the flow-ratein the intestine, and P is the permeability of the membrane comprisingof the villi and microvilli.

The number of particles that have diffused into a blood vessel (e.g.,mesenteric artery), n, in a given time window, Δt, may be modeled as([1,3])

n=P2πrcΔt   (2)

The particles that may have transferred into the blood vessel from theintestine may be advected using the blood velocity streamlines using theNavier-Stokes equation,

$\begin{matrix}{\frac{\partial c_{m}}{\partial t} = {{- v_{m}}\frac{\partial c_{m}}{\partial x}}} & (3)\end{matrix}$

where v_(m) is the blood velocity, and c_(m) is the concentration ofnutrients in the mesenteric artery that may depend on both the locationalong the mesenteric artery and the phase in the blood flow cycle.Population averaged values of the permeability (P) and convective flow(Q) may be obtained. The rest of the parameters may be derived fromeither the image segmentation (e.g. A, r), described in detail herein,or from solving fluid-flow equations (e.g. v_(m)).

However, in some embodiments, the nutrient transport may be modeledbased on various factors, including, but not limited to: the blood flow,blood pressure, and other hemodynamic characteristics of the mesentericartery, iliac artery, celiac artery, and vessels emanating from them(e.g., portal veins, capillaries, etc.); the density, shape, content,and/or nutrients contained in the food as it passes through the GItract, or the temporal and/or environmental aspects during the intake offood (“food intake information”); and gastrointestinal healthinformation, including the health and/or functional capability ofvarious parts of the GI tract, the peristaltic function of theintestines, and properties of the blood vessel membrane affecting thetransfer of nutrients, e.g., membrane channel permeability. Method 400,depicted in FIG. 4, describes an exemplary embodiment of the process ofmodeling nutrient transport (e.g., step 310) in further detail.

Step 312 may include modeling blood flow through, for example, a hepaticand/or portal vessels. Step 312 may further include modeling the phasicchanges in blood flow in relation to food intake (or the digestiveprocess). Blood flow in the portal vein, e.g., the blood flow obtainedfrom step 308, may be used to compute the transport of nutrients intothe liver by solving particle advection through the blood, e.g.,

${\frac{\partial c_{m}}{\partial t} = {{- v_{m}}\frac{\partial c_{m}}{\partial x}}},$

where v_(m) is the blood velocity, and c_(m) is the concentration ofnutrients in the mesenteric artery that may depend on the location, x,along the mesenteric artery and/or on the phase in the blood flow cycle.In some embodiments, the blood flow through the hepatic and/or portalvessels may depend on liver function (e.g., an indicia of a liverfunction), which may be assumed to be normal. If there is evidence ofabnormal liver function (e.g., if there is an indication of insulinresistance), the effective energy supply of the nutrients may becalculated. The transport of nutrients to the systemic circulation maythen be calculated using blood velocity in the hepatic vein.

Step 314 may include determining and outputting relevant quantities ofinterest from method 300. For example, step 314 may include determiningand outputting one or more of the net energy available in the systemiccirculation, the net nutrient availability in the blood stream, thebasal metabolic rate, and/or the insulin resistance. In someembodiments, the net energy available in the systemic circulation may becalculated using output from step 312 (e.g., the transport of nutrientsto the systemic circulation). Since mesenteric ischemia, peristalticdysfunction, and celiac disease may often be accompanied by weight loss,one embodiment may include modeling peristalsis (e.g., smooth musclecontraction (SMC)) and how blood flow affects peristalsis, or the flowrate in the presence of ischemia or celiac disease. Weight gain maydepend on various factors, including, but not limited to, metabolismand/or insulin resistance. In some embodiments, the net nutrientavailability in the blood stream may be calculated using the output fromstep 312 (e.g., the transport of nutrients to the systemic circulation)and may be converted to calories. The basal metabolic rate may becalculated from age, sex, height, weight, etc. The insulin resistancemay be calculated from a fasting glucose test. In some embodiments, therelevant quantities of interest, as presently described may be output toan electronic storage medium or display.

The quantities of interest, including the effective permeability andflowrate, the rate of particle transfer to the mesenteric artery, andthe concentration of nutrients along all the vessels, which may havebeen modeled and/or determined in the preceding steps of method 300, maybe output to an electronic storage medium or display. In addition, thenet energy available in systemic circulation and baseline metabolicdemand, which may have been calculated in step 314, may also be output.Weight loss or gain may be calculated as the difference in net energyavailable in systemic circulation and the baseline metabolic demand.This difference, which may be calculated in calories, may be convertedinto pounds, kilograms, and/or other metrics, and may be output. Apositive value for the difference may imply a weight gain, and anegative value of the difference may imply a weight loss.

FIG. 4 is a block diagram of a general method 400 of modeling nutrientintake from parts of the gastrointestinal (GI) tract (e.g., the smalland large intestines) to the blood vessels (e.g., the portal vein).Method 400 may describe the process of performing step 310 in method 300in further detail. As described in step 310 of method 300, theconcentration of nutrients as it passes through the intestine may bemodeled using the following differential equation that combinesconvection and dispersion of the nutrients within the intestine with thedispersion of nutrients across the villi and microvilli,

${{A\frac{dc}{dt}} = {{AD\frac{d^{2}c}{dt^{2}}} - {Q\frac{dc}{dx}} - {Pc\frac{dA}{dr}}}},$

where c is the concentration of nutrients in the intestine, A is thecross sectional area, D is the molecular diffusivity, Q is the flow-ratein the intestine and P is the permeability of the membrane comprising ofthe villi and microvilli. However, various factors may influence theamount and the way nutrient is transferred to the blood vessels from theGI tract, including, but not limited to: the blood flow, blood pressure,and other hemodynamic characteristics of the mesenteric artery, iliacartery, celiac artery, and vessels emanating from them; the density,shape, content, and/or nutrients contained in the food as it passesthrough the GI tract; the health and/or functional capability of variousparts of the GI tract, including the peristaltic function of theintestines; and properties of the blood vessel membrane affecting thetransfer of nutrients, e.g., membrane channel permeability. While steps402A-D include receiving information on at least some factors that mayinfluence the transfer of nutrients from the GI tract to the bloodvessels, it is contemplated that the factors for which information isreceived in step 402A-D are not exhaustive.

For example, step 402A may include receiving information on fooddensity. Since liquids tend to pass faster than solids, contact timewith the villi and microvilli may be proportional to the density of foodand may be modeled as such. This implies that the flowrate, Q in theabove recited differential equation, may be modeled as inverselyproportional to the food density.

The nutrient transport from the GI tract (e.g., intestines) into theblood vessels may also depend on the blood pressure, blood flow, andother hemodynamic characteristics of the blood vessels leading to thepart of the GI tract where the nutrient transfer is taking place. Thus,step 402B may include receiving information on blood flow through themesenteric vessels (e.g., from step 308 of method 300 as depicted inFIG. 3).

In some embodiments, the nutrient transport may be modeled based onwhether peristaltic dysfunction exists. Thus, step 402C may includereceiving information on the peristaltic function of the GI tract (e.g.,the peristaltic function of the small intestines). For example, ifevidence for peristaltic dysfunction may be found, the nutrient intakemay be accounted, e.g., by scaling by a peristaltic factor(α_(perist)).Other non-linear conversions of the intake factor may alsobe used. Therefore, in one embodiment, if evidence of peristalticdysfunction is found, the flowrate Q may be calculated asQ≡α_(perist)f(W_(norm)). If evidence of peristaltic dysfunction is notfound, the flowrate may be Q≡Q_(norm).

Step 404A may include modeling the flow rate, Q, based on the receivedinformation, including, for example, the information on blood flowthrough the mesenteric vessels (e.g., from step 308), food density, andperistaltic function. Other factors, not described in steps 402A-C, mayalso influence the flow rate, Q. For example, the content of food maydirectly or inversely affect the flow rate, depending on, for example,lipid content.

In some embodiments, the nutrient transport and/or net energy transferfrom the food intake to the portal vein may depend on the concentrationgradient and/or on the channel permeability. Thus, step 402D may includereceiving information on membrane channel permeability from populationstudies and/or the patient. The channel permeability may also depend onthe health of the villi and/or microvilli, for example, on peristalticfunction, as determined in step 402C. Thus, step 404B may includemodeling the membrane channel permeability based on the receivedinformation on the peristaltic function and membrane channelpermeability. In some embodiments, the villi and micro-villi may beassumed to function normally. If there is evidence of disease, such asceliac disease, then there may be a reduced efficiency of energytransfer from the small intestine to the mesenteric artery. In suchembodiments, a corrective factor for permeability through the membranemay be used to model this reduced efficiency. A simple linear model or anon-linear model may be used. In such embodiments, the permeability maybe modeled as P≡α_(perm)f (P_(norm)). In some embodiments, if evidenceof peristaltic dysfunction is not found, the permeability may be modeledas P≡P_(norm).

The concentration gradient may depend on the blood flow, blood pressure,and/or hemodynamic characteristics of the blood vessels, as may bereceived in step 402B (or from step 308) and/or on the flowrate, Q, asmodeled in step 404A. Thus, step 406 may include modeling theconcentration gradient of nutrients between the GI tract and the portalvessels based on the flow rate, Q, and the membrane channelpermeability.

Step 408 may include determining and outputting the net energy transferfrom the food intake to the portal vessels. The net energy transfer maydepend on the transfer of nutrients from the small intestines to theblood vessels (e.g., mesenteric vessels, iliac vessels, celiac vessels,portal vessels, capillaries, etc.), and may be converted to calories.Furthermore, the net energy transfer may depend on the type, amount,form, and/or density of nutrients being transferred. The net particlestransferred in a given time window, Δt, may be modeled by solvingn=P2πrcΔt, where c is the concentration of nutrients in the intestine, Pis the membrane permeability that may be derived from populationaveraged values of membrane permeability or from one or more patients,and the radius, r, may be derived from either the anatomical informationand/or images received in step 302, for example. In some embodiments,the concentration of nutrients in the blood may be assumed to be zero.

While method 400 demonstrates at least some embodiments of the processof determining the transfer of nutrients and/or net energy transfer fromthe food intake in the small intestines to some blood vessels (e.g.,mesenteric vessels, celiac vessels, iliac vessels, portal vessels,vessels in the villi and microvilli, etc.), it is contemplated that thetransfer may occur between any part of the GI tract (stomach, largeintestines, etc.) to any blood vessel involved with the transfer ofnutrients. In such contemplated embodiments, similar steps as laid outin method 400 may be applied.

FIG. 5 is a block diagram of a general method 500 of modeling blood flowthrough the hepatic and portal vessels, and the phasic changes in bloodflow in relation to food intake. Method 500 may describe the process ofperforming steps 312 and 314 in method 300 in further detail.

Step 502A may include receiving information on blood flow, bloodpressure, and/or other hemodynamic characteristics in the blood vesselsdelivering blood to and/or originating from the liver (e.g., as in step308 of method 300 as described in FIG. 3). The blood vessels mayinclude, for example, the hepatic vessels delivering blood from theabdominal aorta to the liver, the hepatic veins carrying filtered bloodand nutrients from the liver on to the systemic circulation, and/or theportal vessels delivering blood and nutrients from parts of the GI tract(e.g., small intestine) to the liver. Step 502A may include, forexample, calculating the blood velocity in the hepatic arteries and/orhepatic veins.

In some embodiments, the transport of nutrients into the liver maydepend on the health and/or functional capability of the liver. Thus,step 502B may include receiving information on the liver function of thepatient. The liver function may include, for example, existence andseverity of diabetes, or insulin resistance. If there is evidence ofabnormal liver function (e.g., if there is an indication of insulinresistance), the effective energy supply of the nutrients may becalculated.

In some embodiments, the transport of nutrients into the liver may bebased on the transfer of nutrients and/or net energy transfer from partsof the GI tract (e.g., the small intestine) to blood vessels (e.g.,vessels in the villi, microvilli, portal veins, etc.). Thus, step 502Cmay include receiving information on the net energy transfer (or anindicia of energy transfer) from the food intake to the blood vesselsleading to the liver (e.g., portal vein). The information may bereceived, for example, from method 400 described in FIG. 4 or from step310 of method 300, as described in FIG. 3.

Step 504A may include modeling the transport of nutrients into the liverbased on one or more of the received information on blood flow, liverfunction, and energy transfer. The transport of nutrients into the livermay be modeled by solving particle advection through the blood, e.g.,

${\frac{\partial c_{m}}{\partial t} = {{- v_{m}}\frac{\partial c_{m}}{\partial x}}},$

where v_(m) is the blood velocity, and c_(m) is the concentration ofnutrients in a blood vessel (e.g., mesenteric artery, portal vein, etc.)leading to the liver that may depend on the location, x, along the bloodvessel and/or on the phase in the blood flow cycle. The health and/orfunctional capability of the liver may influence the transport ofnutrients, for example, by affecting the effective energy supply of thenutrients.

Step 506 may include modeling the transport of nutrients into thesystemic circulation. The transport of nutrients into the systemiccirculation may depend, in part, on the blood flow, blood pressure,and/or hemodynamic characteristics (e.g., blood velocity) of bloodvessels emanating from the liver (e.g., hepatic vein). Thus step 506 maybe based, in part, on step 502A. Alternatively or additionally, in someembodiments, modeling of the transport of nutrients may depend onreceiving information on blood flow (e.g., blood velocity) in thehepatic vein (e.g., as in step 504B or step 308 from FIG. 3).

Step 508A may include receiving information on the basal metabolic rateand/or demand. The information may be received from the patient or frompopulation studies. Alternatively or additionally, the basal metabolicrate and/or demand may be determined based on received informationincluding, but not limited to the age, mass, weight, gender, height,genetic information, body fat percentage, etc. of the patient. Step 508Bmay include determining the net energy available (or an indicia ofenergy available) in the systemic circulation. The net energy availablein the systemic circulation may be based on the transport of nutrientsinto the systemic circulation, modeled in step 506. The net energyavailable or indicia of energy available in the systemic circulation ora vasculature (e.g., visceral vascular system) may include, for example,a net nutrient available in the systemic circulation or a vasculature, anet sugar level in the systemic circulation or a vasculature, energytransferred in the visceral vascular system, and/or an indicia of acertain nutrient or metabolic process in a systemic circulation or avasculature (e.g., chylomicrons). For example, chylomicrons found in theportal vessels may indicate fat-filled particles, which may cause theportal blood to appear milky white after a meal.

Step 510 may include determining the change in weight of the patient.Weight loss or gain may be calculated as the difference in net energyavailable in systemic circulation and the baseline metabolic demand.This difference, which may be calculated in calories, may be convertedinto pounds, kilograms, and/or other metrics, and may be output. Apositive value for the difference may imply a weight gain, and anegative value of the difference may imply a weight loss.

In some embodiments, in addition the basal metabolic rate and/or demandand the net energy available in the systemic circulation, the change inweight may depend on other factors, for example, insulin resistance ofthe patient (e.g., for diabetic patients), and/or information onmesenteric ischemia (e.g., for patients suffering from mesentericischemia). In such embodiments, step 508C may include receivinginformation on insulin resistance. Additionally or alternatively, step508D may include receiving information on mesenteric ischemia.

FIG. 6 is a block diagram of an exemplary method of training andapplying a machine learning algorithm using boundary conditions to solvefor blood flow and blood pressure, according to an exemplary embodimentof the present disclosure. FIG. 6 may depict an exemplary method ofperforming step 308 of method 300 in FIG. 3.

The boundary conditions provide information about the anatomical modelat its boundaries, e.g., the inflow boundaries or inlets, the outflowboundaries or outlets, the vessel wall boundaries, etc. Information ateach boundary may include, e.g., a prescribed value or field forvelocity, flow rate, pressure, or other characteristic, for example, bycoupling a heart model and/or a lumped parameter model to the boundary,etc. Method 600 of FIG. 6 may be performed by server systems 106, basedon information received from physicians 102 and/or third party providers104 over electronic network 100.

In one embodiment, the method 600 of FIG. 6 may include a trainingmethod 602, for training one or more machine learning algorithms basedon boundary conditions measured, estimated, simulated, and/or obtainedfrom numerous patients, and the measured, estimated, simulated, and/orobtained blood flow and/or blood pressure at one or more points of themodel, and a production method 604 for using the machine learningalgorithm results to solve for the blood flow and/or blood pressure atone or more points of the model, and/or the entire system represented bythe model (e.g., as in step 308 of method 300, as described in FIG. 3).

In one embodiment, training method 602 may involve acquiring, for eachof a plurality of individuals, e.g., in digital format: (a) ananatomical model encompassing vessels of interest (e.g., mesentericartery, celiac artery, iliac artery, portal vein, hepatic artery,hepatic vein, etc.), (b) one or more measured, estimated, simulated,and/or obtained boundary conditions (e.g., at the outflow boundaries,inflow boundaries, vessel wall boundaries, etc.) and (c) the measured,estimated, simulated, and/or or obtained blood flow and/or bloodpressure at one or more points of the model and/or entire systemrepresented by the model. Training method 602 may then involve, for oneor more points or boundaries in each patient's model, creating a featurevector of the patients' boundary conditions at one or more points orboundaries of the anatomical model and associating the feature vectorwith the blood flow and/or blood pressure values at one or more pointsof the model or the system represented by the model. Training method 602may then save the results of the machine learning algorithm, includingfeature weights, in a storage device of server systems 106. The storedfeature weights may define the extent to which boundary conditionsand/or anatomical characteristics are predictive of the blood flowand/or blood pressure at one or more points of the model or the systemrepresented by the model.

In one embodiment, the production method 604 may involve estimatingblood flow and/or blood pressure values for a particular patient, basedon results of executing training method 602. In one embodiment,production method 604 may include acquiring, e.g. in digital format: (a)a patient-specific anatomical model encompassing vessels of interest ofthe patient (e.g., mesenteric vessels, celiac vessels, iliac vessels,portal vessels, hepatic vessels, etc.), and (b) one or more measured,estimated, simulated, and/or obtained boundary conditions (e.g., at theinflow boundaries, outflow boundaries, vessel wall boundaries, etc.).For multiple points or boundaries in the patient's anatomical model,production method 604 may involve creating a feature vector of theboundary conditions used in the training mode. Production method 604 maythen use saved results of the machine learning algorithm to solve forblood flow and/or blood pressure at one or more points of the model orthe system represented by the model (e.g., to perform step 308 of method300 as described in FIG. 3). Finally, production method 604 may includesaving the results of the machine learning algorithm, including thesolved blood flow and/or blood pressure, to a storage device of serversystems 106.

The embodiments of the disclosure include systems and methods ofidentifying lesions causing weight loss and/or weight gain (e.g.,mesenteric ischemia, obstruction in the portal venous system or hepaticartery, etc.), and for treatment planning to stent the appropriateregions of vessel narrowing to restore blood flow.

FIG. 7 is a block diagram of an exemplary method of determining lesionsthat result in weight loss (e.g., via mesenteric, hepatic, and/or portalischemia), according to an exemplary embodiment of the presentdisclosure.

In one embodiment, step 702 may include receiving an anatomical modelencompassing vessels of interest (e.g., as in step 302 of method 300 inFIG. 3). Thus, the anatomical model may be received from or generatedfrom anatomical information and/or images of the vessels of interest.Notably, some of the vessels of interest may include lesions. Subsequentsteps of method 700 may aid in the treatment planning of one or more ofthe lesions via a virtual anatomical model.

Step 704 may include identifying a plurality of lesions to be analyzed,for example, to determine which of the plurality of lesions result inweight loss. The lesions may be identified from the anatomical model orinformation received in step 702. In some embodiments, step 704 mayinclude identifying all or one or more combinations of lesions using theanatomical information. Step 704 may be performed by a processor ofserver system 106.

Step 706A may include selecting one or more lesions of (e.g., a subsetof) the identified lesions from step 704. Subsequently, step 708A mayinclude determining the healthy diameter for the vessel at the locationof one or more selected lesions. The healthy diameter may be determinedby evaluating the diameter at proximal and distal vessel locations whichare healthy.

Step 710A may include generating a virtual anatomical model thatrepresents a treatment of the selected one or more lesions. Theappropriate treatment may be based on the determined healthy diametersfrom step 708A.

Step 712A may include determining the net energy available in thesystemic circulation, based on the treatment of the one or more lesions,and outputting that determination into an electronic storage medium. Insome embodiments, step 712A may be performed using the steps 304 through314 of method 300 as depicted in FIG. 3. For example, the virtualanatomical model generated in step 712A, which represents a treatment ofthe selected one or more lesions of the identified lesions using thedetermined healthy diameters, may be truncated at locations to applyappropriate boundary conditions (e.g., as in steps 304 and 306). Thenthe truncated virtual anatomical model may be used to solve for bloodflow and blood pressure through the entire system (e.g., as in step308), model nutrient transport (e.g., as in step 310), and model bloodflow through hepatic vessels and model phasic changes in blood flow inrelation to food intake (e.g., as in step 312), in order to determineand output the net energy available in the systemic circulation (e.g.,as in step 314), based on the treatment of the selected one or morelesions of the identified lesions. Step 712A may further include storingthe determined and output net energy available into an electronicstorage medium. In some embodiments, other relevant quantities may alsobe determined and output into an electronic storage medium. The storedvalues may be used to compare the net energy available in the systemiccirculation, based on the treatment of other selected one or morelesions. Thus, step steps 708A through 712A may be repeated for otherselections of one or more lesions of the identified lesions (e.g., as insteps 706B through steps 712B and step 714), and the outputted netenergies available in the systemic circulation for each selection may becompared and/or assessed (e.g., as in step 714).

For example, step 706B may include selecting another one or more lesionsof the identified lesions from step 704. One or more of the lesionsselected in step 706B may be different from the selected one or morelesions of step 706A, for example, to determined the lesion or group oflesions, whose treatment yields the maximum energy available in thesystemic circulation. Step 708B may include determining the healthydiameter for the selected one or more lesions in step 706B (e.g., as instep 708A). Step 710B may include generating a virtual anatomical modelthat represents a treatment of the one or more lesions using thedetermined healthy diameters (e.g., as in 710A). Likewise, step 712B mayinclude determining the net energy available in the systemic circulation(e.g., as in step 712A), based on the one or more lesions selected instep 706B.

Step 714 may include determining whether the treatment of the selectedone or more lesions (e.g., from step 706B) yields a desired and/ormaximum net energy available in the systemic circulation. For example,step 714 may include comparing the net energy available in the systemiccirculation, as calculated in step 712B for the selected one or morelesions from step 706B, with the net energy available in the systemiccirculation, as calculated in step 712A for the selected one or morelesions from step 706A. The values of the net energies, or otherrelevant quantities of interest to be compared, may be retrieved from anelectronic storage medium of server systems 106. In some embodiments,determining the maximum net energy available may involve determiningwhether among the calculated net energies available, the most recentlycalculated net energy (e.g., from step 712B) trumps any previouslycalculated net energy (e.g., from step 712A) that holds as record as thehighest or maximum net energy available in the systemic circulation.Thus, in such embodiments, the electronic storage medium may mark ordesignate a selected group of lesions, whose treatment yields thehighest or maximum net energy available in the systemic circulation, andthe value of the net energy, as a record. Furthermore, in suchembodiments, if the most recently calculated net energy (e.g., from step712B) trumps any previously held record (e.g., the net energy calculatedin 712A), the most recently calculated net energy may replace anypreviously calculated net energy to hold the record for the highest ormaximum net energy available in the systemic circulation. Importantly,in replacing the record, the selected one or more lesions, whosetreatment yields the highest or maximum net energy available in thesystemic circulation may also be recorded in the electronic storagemedium of server systems 106.

If, subsequent to step 714, the treatment of the selected one or morelesions of (e.g., most recently selected in step 706B) does not yieldthe highest or maximum net energy available in the systemic circulation(e.g., does not beat the currently held record), steps 706B through 712Bmay be repeated, using another set of one or more lesions of theidentified lesions. Subsequently, step 714 may include determiningwhether the treatment of yet another group one or more lesions yieldsthe highest or maximum net energy available in the systemiccirculations.

If, subsequent to step 714, the treatment of a recently selected one ormore lesions does yield the maximum energy available in the systemiccirculation, step 716 may include determining whether all or asufficient number of the identified lesions and/or groups of identifiedlesions have been selected (and/or analyzed using steps 706B through714). If not, steps 706B through step 714 may be performed for multipleiterations until all or a sufficient number of the identified lesions orgroups of lesions have been selected.

If, subsequent to step 716, all or a sufficient number of the identifiedlesions and/or groups of lesions from step 704 have been selected(and/or analyzed using steps 706B through 714), step 718 may includeoutputting one or more of the lesions, whose treatment yields thehighest or maximum energy available in the systemic circulation to anelectronic storage medium or display. In some embodiments, step 718 mayinvolve outputting the currently held record for the maximum net energyavailable in the systemic circulation and the one or more lesions whosetreatment yields that net energy. In some embodiments, step 718 mayadditionally include determining and outputting the difference betweenthe net energy available in the systemic circulation of an untreatedpatient and the net energy available in the systemic circulation of thea treated patient (e.g., as determined in step 712A or 712B). Thus, step718 may include, for example, selecting a combination of one or morelesions, whose difference calculated in step 716 is the largest. In someembodiments, the virtual model of the one or more lesions yielding thehighest or maximum net energy available in the blood stream may also beoutput. In such embodiments, the virtual model may be overlaid with, forexample, blood flow characteristics at one or more points of the model.

In other embodiments, method 700 may be adjusted in order to determineone or more lesions, whose treatment yields the highest or maximum valueof another relevant quantity of interest (e.g., effective permeabilityand flowrate, the rate of particle transfer to a blood vessel, theconcentration of nutrients along vessels, a blood flow characteristic,etc.).

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-20. (canceled)
 21. A computer-implemented method for processingelectronic images to plan treatment of a lesion, the method comprising:receiving, in an electronic storage medium, a patient-specificanatomical model of at least a portion of a visceral vascular system ofa patient; identifying one or more lesions in the patient-specificanatomical model suspected of affecting blood flow to at least a part ofa gastrointestinal system of the patient; determining a healthy diameterof a blood vessel lumen at a location of each of the one or morelesions; generating an anatomical model of a treatment of the one ormore lesions using the determined healthy diameter; and determining andoutputting an indicia of an energy available in the patient, based onthe treatment of the one or more lesions.
 22. The computer-implementedmethod of claim 21, further comprising: identifying and outputting atleast one of the one or more lesions for which treatment yields amaximum net energy available in the patient.
 23. Thecomputer-implemented method of claim 21, wherein determining the indiciaof the energy available in the patient comprises: receiving, in theelectronic storage medium, patient-specific information related to afood intake of the patient; generating a patient-specific model of bloodflow in the anatomical model that represents the treatment of the one ormore lesions; generating a patient-specific model of nutrient transportfrom at least a part of a gastrointestinal system of the patient to thevisceral vascular system of the patient based on the patient-specificinformation related to the food intake of the patient; and determiningthe indicia of the energy available in the patient based on thepatient-specific model of nutrient transport.
 24. Thecomputer-implemented method of claim 23, further comprising: simulatingblood flow through hepatic and portal blood vessels of the patient basedon the patient-specific model of nutrient transport, wherein the bloodflow through the hepatic and portal blood vessels transports nutrients,and wherein determining the indicia of the energy available in thepatient is based on the simulated blood flow and the patient-specificmodel of nutrient transport.
 25. The computer-implemented method ofclaim 23, further comprising: receiving an indicia of a liver functionof the patient, wherein determining an indicia of the energy availablein the patient is based on the indicia of the liver function of thepatient and the patient-specific model of nutrient transport.
 26. Thecomputer-implemented method of claim 23, wherein generating thepatient-specific model of blood flow in the anatomical model comprises:truncating the anatomical model at locations; applying boundaryconditions at the locations to determine blood flow characteristics inthe truncated anatomical model; and generating a patient-specific modelof blood flow based on the determined blood flow characteristics in thetruncated anatomical model.
 27. The computer-implemented method of claim23, wherein the indicia of energy available includes one or more of: anet energy available in a systemic circulation, a net nutrient availablein the systemic circulation, or a net sugar level in the systemiccirculation.
 28. The computer-implemented method of claim 23, whereinpatient-specific information related to the food intake of the patientincludes on one or more of: a density, an amount, a volume, a mass, anutritional content, or an acidity of the food intake of the patient;and temporal and/or environmental information of the food intake by thepatient.
 29. The computer-implemented method of claim 23, furthercomprising receiving gastrointestinal health information of the patient;generating the patient-specific model of nutrient transport from the atleast the part of the gastrointestinal system of the patient to thevisceral vascular system of the patient based on the patient-specificinformation related to the food intake of the patient and thegastrointestinal health information of the patient; and determining theindicia of the energy available in the patient based on thepatient-specific model of nutrient transport and the gastrointestinalhealth information of the patient.
 30. The computer implemented methodof claim 29, wherein the gastrointestinal health information includesone or more of: an indicia of a peristaltic function of agastrointestinal tract of the patient; an estimated membrane channelpermeability of the patient; or an indicia of mesenteric ischemia of thepatient.
 31. The computer implemented method of claim 23, whereingenerating the patient-specific model of nutrient transport from the atleast the part of the gastrointestinal system of the patient to thevisceral vascular system of the patient includes one or more of:modeling a mesenteric flow rate based on one or more of thepatient-specific information related to the food intake of the patientand gastrointestinal health information of the patient; modeling amembrane channel permeability of a vessel; or modeling a concentrationgradient of nutrients between the at least the part of thegastrointestinal system of the patient and a visceral vessels based onone or more of the mesenteric flow rate and the membrane channelpermeability.
 32. The computer-implemented method of claim 21, whereindetermining a healthy diameter of a blood vessel lumen at a location ofeach of the one or more lesions comprises: determining a diameter atvessel locations that are proximal to or distal to the one or morelesions of the identified one or more lesions.
 33. Thecomputer-implemented method of claim 22, further comprising: determininga change in a weight or mass of the patient based on the treatment ofthe identified one or more lesions, whose treatment yields the maximumnet energy available in a systemic circulation of the patient.
 34. Thecomputer-implemented method of claim 33, wherein the change in theweight or mass of the patient is further based on one or more of: ametabolic rate and/or metabolic demand of the patient; and an indicia ofinsulin resistance of the patient.
 35. A system for processingelectronic images to plan treatment of a lesion, the system comprising:a data storage device storing instructions for planning treatment of alesion; and a processor configured to execute the instructions toperform a method comprising: receiving, in an electronic storage medium,a patient-specific anatomical model of at least a portion of a visceralvascular system of a patient; identifying one or more lesions in thepatient-specific anatomical model suspected of affecting blood flow toat least a part of a gastrointestinal system of the patient; determininga healthy diameter of a blood vessel lumen at a location of each of theone or more lesions; generating an anatomical model of a treatment ofthe one or more lesions using the determined healthy diameter; anddetermining and outputting an indicia of an energy available in thepatient, based on the treatment of the one or more lesions.
 36. Thesystem of claim 35, further comprising: identifying and outputting atleast one of the one or more lesions for which treatment yields amaximum net energy available in the patient.
 37. The system of claim 35,wherein determining the indicia of the energy available in the patientcomprises: receiving, in an electronic storage medium, patient-specificinformation related to a food intake of the patient; generating apatient-specific model of blood flow in the anatomical model thatrepresents the treatment of the one or more lesions; generating apatient-specific model of nutrient transport from at least a part of agastrointestinal system of the patient to the visceral vascular systemof the patient based on the patient-specific information related to thefood intake of the patient; and determining the indicia of the energyavailable in the patient based on the patient-specific model of nutrienttransport.
 38. The system of claim 36, further comprising: determining achange in a weight or mass of the patient based on the treatment of theidentified one or more lesions, whose treatment yields the maximum netenergy available in a systemic circulation of the patient.
 39. Anon-transitory computer readable medium for use on a computer systemcontaining computer-executable programming instructions for a method ofprocessing electronic images to plan treatment of a lesion, the methodcomprising: receiving, in an electronic storage medium, apatient-specific anatomical model of at least a portion of a visceralvascular system of a patient; identifying one or more lesions in thepatient-specific anatomical model suspected of affecting blood flow toat least a part of al gastrointestinal system of the patient;determining a healthy diameter of a blood vessel lumen at a location ofeach of the one or more lesions; generating an anatomical model of atreatment of the one or more lesions using the determined healthydiameter; and determining and outputting an indicia of an energyavailable in the patient, based on the treatment of the one or morelesions.
 40. The non-transitory computer readable medium of claim 39,further comprising: identifying and outputting at least one of the oneor more lesions for which treatment yields a maximum net energyavailable in the patient.